Dewni De Silva
February 01 2022
If there is one thing we have learned over the last few years, consumers prefer personalization, and they yearn for the human touch when they interact with brands. If they do not get that, they will move on to a brand that does. Customers expect a certain amount of guided selling and look out for products that interest and appeal to them.
Today, consumer brands such as Amazon, Flipkart, Facebook (now Meta), Shopify, etc., are trying their level best to woo consumers and partners with products and promotions uniquely customized to their preferences and habits. A McKinsey study revealed that personalization drives performance, and companies that grow faster drive 40 percent more revenue from personalization than their slower-growing counterparts.
The data further shows that when companies personalize communication:
But, the question is, how can businesses augment customer experience seamlessly to scale their business, offer personalized solutions, and grow faster?
Consumer brands face a sea of challenges to engage and retain their customers. Brands need to navigate challenges across operations, marketing efficiency, inventory management, price optimization, logistics, etc. The struggle is real.
Big data enables consumer brands to achieve all of these objectives seamlessly. It helps them anticipate consumer preferences and be prepared. Be it Flipkart’s Big Billion Days, Amazon Great Indian Festival, Netflix recommendations, or the most suited credit card options – the use of data and AI-enabled technologies help these consumer brands create custom recommendations based on their preferences and affinity, resulting in an exclusive experience for individual consumers and improved customer service. In addition, technology such as these helps with forecasting trends and making strategic decisions based on market analysis on the go.
So, what’s stopping consumer brands from leveraging data and AI to power their consumer interactions and boost business efficiency? The answer is, that while organizations have large amounts of data at their disposal, it is not easily accessible to all teams. Data democratization, as the name suggests, is making a large amount of data accessible to relevant teams. If used correctly, it helps scale your business to the next level.
Data democratization essentially means that everybody has access to data. There are no gatekeepers. This free access is also accompanied by arming teams with the knowledge they need to use this data and expedite decision- making to contribute to the company’s growth significantly.
Data-stingy businesses often suffer from the slow decision-making processes made by teams severely restricted in terms of agility. Data democratization can propel businesses to new heights of performance.
Benefits of data democratization include:
With the powerful combination of data and AI at their fingertips, teams can gain deeper insights into their customers. Such technologies can also provide recommendations for the next-best-action. Critical decisions such as the right message, right channel, and time can be optimized to boost efficiency as well as delight consumers.
For example, e-commerce brands can identify customers who buy from a specific luxury brand and personalize offers. Banks can determine customers who have not completed the onboarding journey and eliminate roadblocks to help them move towards completion. Music streaming apps can create custom playlists for each listener based on their preferred music and artists.
In the past, these insights were gathered from multiple platforms, most times with the help of technology or data teams running Big Data queries. The time required to run these queries, draw insights, and then apply them was often long. This meant, that brands could not go to the market faster.
However, today’s modern customer engagement platforms offer the benefits of data democratization and AI within a single dashboard, most times requiring no code. Forward-looking consumer brands have already invested in these capabilities and are seeing quick results.
The banking and financial services sector receives a lot of data from massive amounts of customer interactions and compliance requirements. The industry can leverage data and AI to curate and generate content tailored for each customer. They can ensure the communication is delivered at the right moment and also perform quick and insightful segmentation to predict consumer expectations accurately.
For instance, customer mapping can be done depending on lifestyle needs in the finance industry: insurance, study loans for children, car loans, or credit cards for family members. This would pave the way for better customer interactions and offerings that are timely and useful. Analytics-driven personalized money management offerings could very well become the order of the day.
Retail: The retail sector is highly dependent on customer engagement. Here data democratization helps enhance the customer experience as broader access to these insights helps in the overall strategy. Retail brands can easily bridge the gap between their physical stores and digital assets ensuring a unified customer experience. Right algorithms at the right time are showing the way for retail growth, fulfilling customer needs and aspirations. Successful retail enterprises are customer-centric, offering hyper-personalized solutions to their customers with digital technologies at play, customized content, customized product, enhanced quality, customer rewards, and a holistic customer experience.
A final thought: Having AI and data democratization in place can simplify brands’ data-driven decision-making. It can help ensure scale with confidence and speed. According to Accenture, 72 percent of companies successfully scaling AI in their organization said that a core data foundation was key to their success.
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